Building SimpliInvest: AI Risk Scoring from Zero to Production

The Problem

Every retail investor deserves access to the kind of risk analysis that institutional traders take for granted. But most tools are either too complex, too expensive, or too slow. I wanted to build something that could analyze any stock, ETF, or crypto asset and produce an instant, AI-driven risk score — accessible to anyone with a browser.

The Stack

I chose a stack optimized for speed-to-production and real-time performance:

Next.js 15 with App Router for the frontend and API routes
Firebase (Firestore + App Hosting) for data persistence and deployment
Google Genkit for AI agent orchestration
Multiple market data APIs for live pricing, fundamentals, and sentiment

Architecture Decisions

Why Firebase over a traditional backend?

For a solo builder shipping fast, Firebase eliminates the ops burden. Firestore handles real-time data sync, Auth handles user management, and App Hosting gives me zero-config deployment. The trade-off is vendor lock-in, but for a product at this stage, velocity matters more.

AI Agent Design

The risk scoring engine isn't a single prompt — it's a multi-step agent pipeline:

Data Collection Agent — gathers live price data, fundamentals, news sentiment
Analysis Agent — runs quantitative risk models across multiple timeframes
Scoring Agent — synthesizes into a 1-100 risk score with human-readable explanation

Each agent is independently testable and can be improved without affecting the others.

Key Learnings

Ship the MVP, then iterate. The first version had a single risk model. Now it has multiple analysis dimensions. But the first version shipped in two weeks and immediately validated the concept.

AI agents need guardrails. Without structured output schemas and validation, LLM-powered analysis can drift. I use Zod schemas to enforce output structure at every stage.

Real-time data is hard. Market data APIs have rate limits, inconsistent formats, and occasional outages. Building a resilient data layer with caching and fallbacks was 40% of the engineering effort.

Results

Live at simpliinvest.app — serving real users
Sub-second risk scores for any ticker
Multi-asset support — stocks, ETFs, and crypto
AI-generated explanations that retail investors can actually understand

What's Next

I'm building portfolio-level analysis (not just single tickers), historical backtesting of risk scores, and a mobile-first redesign. The goal is to make SimpliInvest the default risk check before any investment decision.

Building something with AI? Let's talk about how to go from prototype to production.